Format

Send to

Choose Destination
Sci Adv. 2018 Jun 1;4(6):eaar4206. doi: 10.1126/sciadv.aar4206. eCollection 2018 Jun.

Nanophotonic particle simulation and inverse design using artificial neural networks.

Author information

1
Department of Physics, Massachusetts Institute of Technology, Cambridge, MA 02139, USA.
2
Department of Electrical Engineering and Computer Science, Massachusetts Institute of Technology, Cambridge, MA 02139, USA.
3
Department of Mathematics, Massachusetts Institute of Technology, Cambridge, MA 02139, USA.
4
U.S. Army Edgewood Chemical Biological Center, Aberdeen Proving Ground, MD 21010, USA.

Abstract

We propose a method to use artificial neural networks to approximate light scattering by multilayer nanoparticles. We find that the network needs to be trained on only a small sampling of the data to approximate the simulation to high precision. Once the neural network is trained, it can simulate such optical processes orders of magnitude faster than conventional simulations. Furthermore, the trained neural network can be used to solve nanophotonic inverse design problems by using back propagation, where the gradient is analytical, not numerical.

Supplemental Content

Full text links

Icon for PubMed Central
Loading ...
Support Center